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difficult to deploy outside large data centres. This PhD project focuses on developing resource-efficient computer vision methods that maintain strong performance while dramatically reducing computation
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About the project: Machine Learning to Unlock the Mysteries of Metallic Phase Transitions Supervisor: Dr Livia Pártay, University of Warwick Join a PhD project that goes beyond state-of-the-art to
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speed - Provide human experts with a reliable second opinion This project integrates image processing, data analytics, machine learning, and computational modelling, with applications in aerospace
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-scale metagenomic assembly and genome recovery • Comparative genomics and molecular evolution • Machine-learning-based protein prediction • Data integration, bioinformatics and phylogenetics • Scientific
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Productivity Index (RPI) using observed versus potential productivity modelled with machine learning (https://doi.org/10.1016/j.ecolind.2025.113208 ), this applied geospatial ecology project will study how
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in the fields of machine learning, epidemiology, big data analysis, and suicide and self-harm. Your work will expand the frontier of machine learning applications in epidemiology and improve our
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) data. We also analyse macaque electrophysiology data obtained through collaborations. We use machine learning techniques for data analysis and computational modelling with a special interest in
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self-harm rates. You will expand your data wrangling, analytical and programming skills on python and develop expertise in the fields of machine learning, epidemiology, big data analysis, and suicide and
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interaction, signal processing, data science and machine learning. The successful candidate will gain expertise at the intersection of structural health monitoring, railway engineering, and advanced artificial
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their pandemic potential and classification as bioweapons. This project aims to develop a machine learning-accelerated NMR platform for the discovery of high-affinity inhibitors targeting viral RNAPs. Building